

# One seed
CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method RNNSAC \
    --buffer_size 1000 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method ENVDIFF \
    --contrast_training_interval 2 \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --seed 100 \
    --use_weighted_info_nce \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethodOne \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --use_weighted_info_nce \
    --seed 100 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethod \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_weighted_info_nce \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &








# One seed
CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method RNNSAC \
    --buffer_size 1000 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 101 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method ENVDIFF \
    --contrast_training_interval 2 \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --seed 101 \
    --use_weighted_info_nce \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethodOne \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --use_weighted_info_nce \
    --seed 101 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethod \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 101 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_weighted_info_nce \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &




# another seed

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method RNNSAC \
    --buffer_size 1000 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &



CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method ENVDIFF \
    --buffer_size 1000 \
    --contrast_training_interval 2 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --seed 102 \
    --use_weighted_info_nce \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethodOne \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --encoder_tau 0.05 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --use_weighted_info_nce \
    --seed 102 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name PandaPush-v3 \
    --env_hook PandaHook \
    --method OURMethod \
    --buffer_size 1000 \
    --adversarial_loss_coef 0.01 \
    --train_freq 128 \
    --gradient_steps 16 \
    --learning_rate 1e-3 \
    --batch_size 256 \
    --contrast_batch_size 256 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
    --test_eps_num_per_env 100 \
    --use_weighted_info_nce \
    --use_wandb \
    --time_step 1_000_000 \
    --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &









# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method ENVDIFF \
#     --buffer_size 1000 \
#     --contrast_training_interval 2 \
#     --adversarial_loss_coef 0.05 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --seed 103 \
#     --use_weighted_info_nce \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethodOne \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.05 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --use_weighted_info_nce \
#     --seed 103 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethod \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.05 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --encoder_tau 0.05 \
#     --seed 103 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_weighted_info_nce \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &


# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method ENVDIFF \
#     --buffer_size 1000 \
#     --contrast_training_interval 2 \
#     --adversarial_loss_coef 0.1 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --seed 103 \
#     --use_weighted_info_nce \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethodOne \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.1 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --use_weighted_info_nce \
#     --seed 103 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethod \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.1 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --encoder_tau 0.05 \
#     --seed 103 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_weighted_info_nce \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &




# # One seed
# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method RNNSAC \
#     --buffer_size 1000 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --batch_size 256 \
#     --contrast_batch_size 256 \
#     --encoder_tau 0.05 \
#     --seed 102 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method ENVDIFF \
#     --contrast_training_interval 2 \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 128 \
#     --seed 100 \
#     --use_weighted_info_nce \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethodOne \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 128 \
#     --use_weighted_info_nce \
#     --seed 101 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethod \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --batch_size 256 \
#     --contrast_batch_size 128 \
#     --encoder_tau 0.05 \
#     --seed 100 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_weighted_info_nce \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &



# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method ENVDIFF \
#     --contrast_training_interval 2 \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 64 \
#     --seed 100 \
#     --use_weighted_info_nce \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethodOne \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --encoder_tau 0.05 \
#     --batch_size 256 \
#     --contrast_batch_size 64 \
#     --use_weighted_info_nce \
#     --seed 101 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &

# CUDA_VISIBLE_DEVICES=0 nohup python main.py \
#     --env_name PandaPush-v3 \
#     --env_hook PandaHook \
#     --method OURMethod \
#     --buffer_size 1000 \
#     --adversarial_loss_coef 0.01 \
#     --train_freq 128 \
#     --gradient_steps 16 \
#     --learning_rate 1e-3 \
#     --batch_size 256 \
#     --contrast_batch_size 64 \
#     --encoder_tau 0.05 \
#     --seed 100 \
#     --test_envs "[(0.1, 1),(0.1, 5),(0.1, 10),(0.1, 30),(1, 1),(1, 5),(1, 10),(1, 30),(5, 1),(5, 5),(5, 10),(5, 30),(10, 1),(10, 5),(10, 10),(10, 30),(30, 1),(30, 5),(30, 10),(30, 30)]" \
#     --test_eps_num_per_env 100 \
#     --use_weighted_info_nce \
#     --use_wandb \
#     --time_step 1_000_000 \
#     --train_envs "[(0, 30),(0, 10),(0, 5),(1, 1),(1, 1),(5, 1),(10, 1),(30, 1)]" &
